720 research outputs found
The Obliteration of Truth by Management: Badiou, St. Paul and the Question of Economic Managerialism in Education
This paper considers the questions that Badiou’s theory of the subject poses to cultures of economic managerialism within education. His argument that radical change is possible, for people and the situations they inhabit, provides a stark challenge to the stifling nature of much current educational climate. In 'Saint Paul: The Foundation of Universalism', Badiou describes the current universalism of capitalism, monetary homogeneity and the rule of the count. Badiou argues that the politics of identity are all too easily subsumed by the prerogatives of the marketplace and unable to present, therefore, a critique of the status quo. These processes are, he argues, without the potential for truth. What are the implications of Badiou’s claim that education is the arranging of ‘the forms of knowledge in such a way that truth may come to pierce a hole in them’ (Badiou, 2005, p. 9)? In this paper, I argue that Badiou’s theory opens up space for a kind of thinking about education that resists its colonisation by cultures of management and marketisation and leads educationalists to consider the emancipatory potential of education in a new light
Cosmic Calibration: Constraints from the Matter Power Spectrum and the Cosmic Microwave Background
Several cosmological measurements have attained significant levels of
maturity and accuracy over the last decade. Continuing this trend, future
observations promise measurements of the statistics of the cosmic mass
distribution at an accuracy level of one percent out to spatial scales with
k~10 h/Mpc and even smaller, entering highly nonlinear regimes of gravitational
instability. In order to interpret these observations and extract useful
cosmological information from them, such as the equation of state of dark
energy, very costly high precision, multi-physics simulations must be
performed. We have recently implemented a new statistical framework with the
aim of obtaining accurate parameter constraints from combining observations
with a limited number of simulations. The key idea is the replacement of the
full simulator by a fast emulator with controlled error bounds. In this paper,
we provide a detailed description of the methodology and extend the framework
to include joint analysis of cosmic microwave background and large scale
structure measurements. Our framework is especially well-suited for upcoming
large scale structure probes of dark energy such as baryon acoustic
oscillations and, especially, weak lensing, where percent level accuracy on
nonlinear scales is needed.Comment: 15 pages, 14 figure
Open TURNS: An industrial software for uncertainty quantification in simulation
The needs to assess robust performances for complex systems and to answer
tighter regulatory processes (security, safety, environmental control, and
health impacts, etc.) have led to the emergence of a new industrial simulation
challenge: to take uncertainties into account when dealing with complex
numerical simulation frameworks. Therefore, a generic methodology has emerged
from the joint effort of several industrial companies and academic
institutions. EDF R&D, Airbus Group and Phimeca Engineering started a
collaboration at the beginning of 2005, joined by IMACS in 2014, for the
development of an Open Source software platform dedicated to uncertainty
propagation by probabilistic methods, named OpenTURNS for Open source Treatment
of Uncertainty, Risk 'N Statistics. OpenTURNS addresses the specific industrial
challenges attached to uncertainties, which are transparency, genericity,
modularity and multi-accessibility. This paper focuses on OpenTURNS and
presents its main features: openTURNS is an open source software under the LGPL
license, that presents itself as a C++ library and a Python TUI, and which
works under Linux and Windows environment. All the methodological tools are
described in the different sections of this paper: uncertainty quantification,
uncertainty propagation, sensitivity analysis and metamodeling. A section also
explains the generic wrappers way to link openTURNS to any external code. The
paper illustrates as much as possible the methodological tools on an
educational example that simulates the height of a river and compares it to the
height of a dyke that protects industrial facilities. At last, it gives an
overview of the main developments planned for the next few years
Multivariate analysis using high definition flow cytometry reveals distinct T cell repertoires between the fetal–maternal interface and the peripheral blood
The human T cell compartment is a complex system and while some information is known on repertoire composition and dynamics in the peripheral blood, little is known about repertoire composition at different anatomical sites. Here, we determine the T cell receptor beta variable (TRBV) repertoire at the decidua and compare it with the peripheral blood during normal pregnancy and pre-eclampsia. We found total T cell subset disparity of up to 58% between sites, including large signature TRBV expansions unique to the fetal–maternal interface. Defining the functional nature and specificity of compartment-specific T cells will be necessary if we are to understand localized immunity, tolerance, and pathogenesis
Bayesian optimization for materials design
We introduce Bayesian optimization, a technique developed for optimizing
time-consuming engineering simulations and for fitting machine learning models
on large datasets. Bayesian optimization guides the choice of experiments
during materials design and discovery to find good material designs in as few
experiments as possible. We focus on the case when materials designs are
parameterized by a low-dimensional vector. Bayesian optimization is built on a
statistical technique called Gaussian process regression, which allows
predicting the performance of a new design based on previously tested designs.
After providing a detailed introduction to Gaussian process regression, we
introduce two Bayesian optimization methods: expected improvement, for design
problems with noise-free evaluations; and the knowledge-gradient method, which
generalizes expected improvement and may be used in design problems with noisy
evaluations. Both methods are derived using a value-of-information analysis,
and enjoy one-step Bayes-optimality
A bounded confidence approach to understanding user participation in peer production systems
Commons-based peer production does seem to rest upon a paradox. Although
users produce all contents, at the same time participation is commonly on a
voluntary basis, and largely incentivized by achievement of project's goals.
This means that users have to coordinate their actions and goals, in order to
keep themselves from leaving. While this situation is easily explainable for
small groups of highly committed, like-minded individuals, little is known
about large-scale, heterogeneous projects, such as Wikipedia.
In this contribution we present a model of peer production in a large online
community. The model features a dynamic population of bounded confidence users,
and an endogenous process of user departure. Using global sensitivity analysis,
we identify the most important parameters affecting the lifespan of user
participation. We find that the model presents two distinct regimes, and that
the shift between them is governed by the bounded confidence parameter. For low
values of this parameter, users depart almost immediately. For high values,
however, the model produces a bimodal distribution of user lifespan. These
results suggest that user participation to online communities could be
explained in terms of group consensus, and provide a novel connection between
models of opinion dynamics and commons-based peer production.Comment: 17 pages, 5 figures, accepted to SocInfo201
Sequential design of computer experiments for the estimation of a probability of failure
This paper deals with the problem of estimating the volume of the excursion
set of a function above a given threshold,
under a probability measure on that is assumed to be known. In
the industrial world, this corresponds to the problem of estimating a
probability of failure of a system. When only an expensive-to-simulate model of
the system is available, the budget for simulations is usually severely limited
and therefore classical Monte Carlo methods ought to be avoided. One of the
main contributions of this article is to derive SUR (stepwise uncertainty
reduction) strategies from a Bayesian-theoretic formulation of the problem of
estimating a probability of failure. These sequential strategies use a Gaussian
process model of and aim at performing evaluations of as efficiently as
possible to infer the value of the probability of failure. We compare these
strategies to other strategies also based on a Gaussian process model for
estimating a probability of failure.Comment: This is an author-generated postprint version. The published version
is available at http://www.springerlink.co
Design of Experiments for Screening
The aim of this paper is to review methods of designing screening
experiments, ranging from designs originally developed for physical experiments
to those especially tailored to experiments on numerical models. The strengths
and weaknesses of the various designs for screening variables in numerical
models are discussed. First, classes of factorial designs for experiments to
estimate main effects and interactions through a linear statistical model are
described, specifically regular and nonregular fractional factorial designs,
supersaturated designs and systematic fractional replicate designs. Generic
issues of aliasing, bias and cancellation of factorial effects are discussed.
Second, group screening experiments are considered including factorial group
screening and sequential bifurcation. Third, random sampling plans are
discussed including Latin hypercube sampling and sampling plans to estimate
elementary effects. Fourth, a variety of modelling methods commonly employed
with screening designs are briefly described. Finally, a novel study
demonstrates six screening methods on two frequently-used exemplars, and their
performances are compared
Nontransgenic models of breast cancer
Numerous models have been developed to address key elements in the biology of breast cancer development and progression. No model is ideal, but the most useful are those that reflect the natural history and histopathology of human disease, and allow for basic investigations into underlying cellular and molecular mechanisms. We describe two types of models: those that are directed toward early events in breast cancer development (hyperplastic alveolar nodules [HAN] murine model, MCF10AT human xenograft model); and those that seek to reflect the spectrum of metastatic disease (murine sister cell lines 67, 168, 4T07, 4T1). Collectively, these models provide cell lines that represent all of the sequential stages of progression in breast disease, which can be modified to test the effect of genetic changes
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